Journal ArticleDOI
Review of photovoltaic power forecasting
J. Antonanzas,Natalia Osorio,Rodrigo Escobar,Ruben Urraca,Francisco Javier Martinez-de-Pison,F. Antonanzas-Torres +5 more
TLDR
This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends, and represents the most up-to-date compilation of solarPower forecasting studies.About:
This article is published in Solar Energy.The article was published on 2016-10-15. It has received 829 citations till now. The article focuses on the topics: Solar power forecasting & Probabilistic forecasting.read more
Citations
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Journal ArticleDOI
Forecasting of photovoltaic power generation and model optimization: A review
Utpal Kumar Das,Kok Soon Tey,Mohammadmehdi Seyedmahmoudian,Saad Mekhilef,Moh Yamani Idna Idris,Willem Van Deventer,Ben Horan,Alex Stojcevski +7 more
TL;DR: In this paper, a comprehensive and systematic review of the direct forecasting of PV power generation is presented, where the importance of the correlation of the input-output data and the preprocessing of model input data are discussed.
Online Short-term Solar Power Forecasting
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
Journal ArticleDOI
Solar photovoltaic generation forecasting methods: A review
TL;DR: In this article, an extensive review on recent advancements in the field of solar photovoltaic power forecasting is presented, which aims to analyze and compare various methods of solar PV power forecasting in terms of characteristics and performance.
Journal ArticleDOI
A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Journal ArticleDOI
Accurate photovoltaic power forecasting models using deep LSTM-RNN
TL;DR: The use of long short-term memory recurrent neural network (LSTM-RNN) to accurately forecast the output power of PV systems and offers a further reduction in the forecasting error compared with the other methods.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
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Solar forecasting methods for renewable energy integration
TL;DR: In this article, the authors review the theory behind these forecasting methodologies, and a number of successful applications of solar forecasting methods for both the solar resource and the power output of solar plants at the utility scale level.
Journal ArticleDOI
A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy
TL;DR: In this paper, a multilayer perceptron (MLP) model was proposed to forecast the solar irradiance on a base of 24h using the present values of the mean daily solar irradiances and air temperature.